The Biological General Repository for Interaction Datasets (BioGRID: https://thebiogrid.org) is an open access database dedicated to the curation and archival storage of protein, genetic and chemical interactions for all major model organism species and humans. As of September 2018 (build 3.4.164), BioGRID contains records for 1 598 688 biological interactions manually annotated from 55 809 publications for 71 species, as classified by an updated set of controlled vocabularies for experimental detection methods. BioGRID also houses records for >700 000 post-translational modification sites. BioGRID now captures chemical interaction data, including chemical–protein interactions for human drug targets drawn from the DrugBank database and manually curated bioactive compounds reported in the literature. A new dedicated aspect of BioGRID annotates genome-wide CRISPR/Cas9-based screens that report gene–phenotype and gene–gene relationships. An extension of the BioGRID resource called the Open Repository for CRISPR Screens (ORCS) database (https://orcs.thebiogrid.org) currently contains over 500 genome-wide screens carried out in human or mouse cell lines. All data in BioGRID is made freely available without restriction, is directly downloadable in standard formats and can be readily incorporated into existing applications via our web service platforms. BioGRID data are also freely distributed through partner model organism databases and meta-databases.

We used synthetic lethal high-throughput screening to interrogate 23,550 compounds for their ability to kill engineered tumorigenic cells but not their isogenic normal cell counterparts. We identified known and novel compounds with genotype-selective activity, including doxorubicin, daunorubicin, mitoxantrone, camptothecin, sangivamycin, echinomycin, bouvardin, NSC146109, and a novel compound that we named erastin. These compounds have increased activity in the presence of hTERT, the SV40 large and small T oncoproteins, the human papillomavirus type 16 (HPV) E6 and E7 oncoproteins, and oncogenic HRAS. We found that overexpressing hTERT and either E7 or LT increased expression of topoisomerase 2alpha and that overexpressing RAS(V12) and ST both increased expression of topoisomerase 1 and sensitized cells to a nonapoptotic cell death process initiated by erastin.

The BioGRID (Biological General Repository for Interaction Datasets, http://thebiogrid.org) is an open‐access database resource that houses manually curated protein and genetic interactions from multiple species including yeast, worm, fly, mouse, and human. The ~1.93 million curated interactions in BioGRID can be used to build complex networks to facilitate biomedical discoveries, particularly as related to human health and disease. All BioGRID content is curated from primary experimental evidence in the biomedical literature, and includes both focused low‐throughput studies and large high‐throughput datasets. BioGRID also captures protein post‐translational modifications and protein or gene interactions with bioactive small molecules including many known drugs. A built‐in network visualization tool combines all annotations and allows users to generate network graphs of protein, genetic and chemical interactions. In addition to general curation across species, BioGRID undertakes themed curation projects in specific aspects of cellular regulation, for example the ubiquitin‐proteasome system, as well as specific disease areas, such as for the SARS‐CoV‐2 virus that causes COVID‐19 severe acute respiratory syndrome. A recent extension of BioGRID, named the Open Repository of CRISPR Screens (ORCS, http://orcs.thebiogrid.org), captures single mutant phenotypes and genetic interactions from published high throughput genome‐wide CRISPR/Cas9‐based genetic screens. BioGRID‐ORCS contains datasets for over 1,042 CRISPR screens carried out to date in human, mouse and fly cell lines. The biomedical research community can freely access all BioGRID data through the web interface, standardized file downloads, or via model organism databases and partner meta‐databases.

SummaryHuman glioblastomas (GBMs) harbour a subpopulation of glioblastoma stem cells (GSCs) that drive tumourigenesis. However, the origin of intra-tumoural functional heterogeneity between GBM cells remains poorly understood. Here we study the clonal evolution of barcoded GBM cells in an unbiased way following serial xenotransplantation to define their individual fate behaviours. Independent of an evolving mutational signature, we show that the growth of GBM clones in vivo is consistent with a remarkably neutral process involving a conserved proliferative hierarchy rooted in GSCs. In this model, slow-cycling stem-like cells give rise to a more rapidly cycling progenitor population with extensive self-maintenance capacity, that in turn generates non-proliferative cells. We also identify rare “outlier” clones that deviate from these dynamics, and further show that chemotherapy facilitates the expansion of pre-existing drug-resistant GSCs. Finally, we show that functionally distinct GSCs can be separately targeted using epigenetic compounds, suggesting new avenues for GBM targeted therapy.